In neural signal processing, the detection or identification of individual neural spikes within sequences of neural signals is important for facilitating or enabling neural signal decoding or interpretation. Unfortunately, neural signals exhibit unpredictable nonlinear behavior, including non-stationary noise, amplitude/waveform fluctuations, and firing state/firing rate transitions. As a result, the accurate and reliable identification of neural spikes is difficult.
In spite of several decades of research, no sufficiently robust neural signal processing technique currently exists that can accurately and reliably identify individual neural spikes within a wide variety of actual or “real world” neural signal sequences about which prior knowledge does not exist, or with respect to which a priori assumptions cannot be made. Furthermore, to date no neural signal processing architecture exists that can readily be scaled for processing neural signal data corresponding to small, moderate, or substantial populations of neurons (e.g., hundreds, thousands, or millions of neurons, respectively).
A need exists for a neural signal processing architecture that overcomes such problems.